Design and Implementation of an Online House Price Prediction System
Computer Science — 2025, Undergraduate
regressionmachine learningpredictionhouse price
This study designed and implemented an Online House Price Prediction System that estimates property values from features such as size, location, and number of rooms. Accurate price estimation is useful to buyers, sellers, and agents in a fluctuating property market. The system trains regression models on historical property records and returns predicted prices with confidence indicators. Built with Python Django and SQLite, the system was evaluated using a dataset of property listings. Findings revealed low prediction error and reliable price estimates. The study recommends integration with real estate platforms and continuous updating of the training data.
Ad slot
Homepage screenshot
